


import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard




@pytest.mark.parametrize(
    '''compression_format, is_archive'''      ,    [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]      ,    )
def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    )	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		{
          '''7z''': (seven_zip_file, SevenZipExtractor),
          '''bz2''': (bza_file, BzipaExtractor),
          '''gzip''': (gz_file, GzipExtractor),
          '''lz4''': (lza_file, LzaExtractor),
          '''tar''': (tar_file, TarExtractor),
          '''xz''': (xz_file, XzExtractor),
          '''zip''': (zip_file, ZipExtractor),
          '''zstd''': (zstd_file, ZstdExtractor),
      }
      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		input_paths_and_base_extractors[compression_format]
      if input_path is None:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(A__							)
      assert base_extractor.is_extractable(A__							)
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
      base_extractor.extract(A__      ,    A__							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		file_path.read_text(encoding='''utf-8'''							)
      else:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		output_path.read_text(encoding='''utf-8'''							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		text_file.read_text(encoding='''utf-8'''							)
      assert extracted_file_content == expected_file_content




@pytest.mark.parametrize(
    '''compression_format, is_archive'''      ,    [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]      ,    )
def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    )	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		{
          '''7z''': seven_zip_file,
          '''bz2''': bza_file,
          '''gzip''': gz_file,
          '''lz4''': lza_file,
          '''tar''': tar_file,
          '''xz''': xz_file,
          '''zip''': zip_file,
          '''zstd''': zstd_file,
      }
      SCREAMING_SNAKE_CASE__       :  int						     =		input_paths[compression_format]
      if input_path is None:
            SCREAMING_SNAKE_CASE__       :  int						     =		f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(A__							)
      SCREAMING_SNAKE_CASE__       :  Any						     =		Extractor.infer_extractor_format(A__							)
      assert extractor_format is not None
      SCREAMING_SNAKE_CASE__       :  Any						     =		tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
      Extractor.extract(A__      ,    A__      ,    A__							)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		file_path.read_text(encoding='''utf-8'''							)
      else:
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		output_path.read_text(encoding='''utf-8'''							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		text_file.read_text(encoding='''utf-8'''							)
      assert extracted_file_content == expected_file_content




@pytest.fixture
def    a   (							A__      ,    A__							)	-> str:




      '''simple docstring'''

      import tarfile

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		tmp_path / '''data_dot_dot'''
      directory.mkdir()
      SCREAMING_SNAKE_CASE__       :  str						     =		directory / '''tar_file_with_dot_dot.tar'''
      with tarfile.TarFile(A__      ,    '''w'''							) as f:
            f.add(A__      ,    arcname=os.path.join('''..'''      ,    text_file.name							)							)
      return path




@pytest.fixture
def    a   (							A__							)	-> Union[str, Any]:




      '''simple docstring'''

      import tarfile

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		tmp_path / '''data_sym_link'''
      directory.mkdir()
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		directory / '''tar_file_with_sym_link.tar'''
      os.symlink('''..'''      ,    directory / '''subdir'''      ,    target_is_directory=A__							)
      with tarfile.TarFile(A__      ,    '''w'''							) as f:
            f.add(str(directory / '''subdir'''							)      ,    arcname='''subdir'''							)  # str required by os.readlink on Windows and Python < 3.8
      return path




@pytest.mark.parametrize(
    '''insecure_tar_file, error_log'''      ,    [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')]      ,    )
def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__							)	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		{
          '''tar_file_with_dot_dot''': tar_file_with_dot_dot,
          '''tar_file_with_sym_link''': tar_file_with_sym_link,
      }
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		insecure_tar_files[insecure_tar_file]
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		tmp_path / '''extracted'''
      TarExtractor.extract(A__      ,    A__							)
      assert caplog.text
      for record in caplog.records:
            assert record.levelname == "ERROR"
            assert error_log in record.msg





def    a   (							A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		tmpdir / '''not_a_zip_file'''
      # From: https://github.com/python/cpython/pull/5053
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		(
          b'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
          b'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
          b'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
          b'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
      )
      with not_a_zip_file.open('''wb'''							) as f:
            f.write(A__							)
      assert zipfile.is_zipfile(str(A__							)							)  # is a false positive for `zipfile`
      assert not ZipExtractor.is_extractable(A__							)  # but we're right











import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   Optional[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			@property
			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		str							      =     1
						UpperCAmelCase_:		Optional[int]							      =     3
						UpperCAmelCase_:		Optional[Any]							      =     (32, 32)

						UpperCAmelCase_:		int							      =     floats_tensor((batch_size, num_channels) + sizes,   rng=random.Random(0					)					).to(SCREAMING_SNAKE_CASE_					)
						return image

			@property
			def      __snake_case       (self					)       ->   str:
						torch.manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     UNetaDConditionModel(
						    block_out_channels=(32, 64),   layers_per_block=2,   sample_size=32,   in_channels=4,   out_channels=4,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),   cross_attention_dim=32,   )
						return model

			@property
			def      __snake_case       (self					)       ->   Optional[int]:
						torch.manual_seed(0					)
						UpperCAmelCase_:		Union[str, Any]							      =     AutoencoderKL(
						    block_out_channels=[32, 64],   in_channels=3,   out_channels=3,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],   latent_channels=4,   )
						return model

			@property
			def      __snake_case       (self					)       ->   Tuple:
						torch.manual_seed(0					)
						UpperCAmelCase_:		List[Any]							      =     RobertaSeriesConfig(
						    hidden_size=32,   project_dim=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=5006,   )
						return RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_					)

			@property
			def      __snake_case       (self					)       ->   Union[str, Any]:
						def extract(*SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					):







									class 				_a				:

												def __init__(self					)       ->   Tuple:
															UpperCAmelCase_:		Any							      =     torch.ones([0]					)

												def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
															self.pixel_values.to(SCREAMING_SNAKE_CASE_					)
															return self






									return Out()

						return extract

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		List[str]							      =     """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Union[str, Any]							      =     self.dummy_cond_unet
						UpperCAmelCase_:		Any							      =     PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     self.dummy_vae
						UpperCAmelCase_:		int							      =     self.dummy_text_encoder
						UpperCAmelCase_:		int							      =     XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""					)
						UpperCAmelCase_:		Tuple							      =     77

						UpperCAmelCase_:		Tuple							      =     self.dummy_image.to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		str							      =     init_image / 2 + 0.5

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		List[Any]							      =     AltDiffusionImgaImgPipeline(
						    unet=SCREAMING_SNAKE_CASE_,   scheduler=SCREAMING_SNAKE_CASE_,   vae=SCREAMING_SNAKE_CASE_,   text_encoder=SCREAMING_SNAKE_CASE_,   tokenizer=SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   feature_extractor=self.dummy_extractor,   )
						UpperCAmelCase_:		Dict							      =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,   do_normalize=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe.to(SCREAMING_SNAKE_CASE_					)
						alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		List[str]							      =     torch.Generator(device=SCREAMING_SNAKE_CASE_					).manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   guidance_scale=6.0,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   )

						UpperCAmelCase_:		List[str]							      =     output.images

						UpperCAmelCase_:		Optional[Any]							      =     torch.Generator(device=SCREAMING_SNAKE_CASE_					).manual_seed(0					)
						UpperCAmelCase_:		Any							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   guidance_scale=6.0,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   return_dict=SCREAMING_SNAKE_CASE_,   )[0]

						UpperCAmelCase_:		List[str]							      =     image[0, -3:, -3:, -1]
						UpperCAmelCase_:		str							      =     image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						UpperCAmelCase_:		Tuple							      =     np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-3
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 5E-3

			@unittest.skipIf(torch_device != """cuda""",   """This test requires a GPU"""					)
			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		List[Any]							      =     self.dummy_cond_unet
						UpperCAmelCase_:		List[Any]							      =     PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     self.dummy_vae
						UpperCAmelCase_:		Optional[Any]							      =     self.dummy_text_encoder
						UpperCAmelCase_:		Dict							      =     XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""					)
						UpperCAmelCase_:		Union[str, Any]							      =     77

						UpperCAmelCase_:		Tuple							      =     self.dummy_image.to(SCREAMING_SNAKE_CASE_					)

						# put models in fp16
						UpperCAmelCase_:		Optional[Any]							      =     unet.half()
						UpperCAmelCase_:		Any							      =     vae.half()
						UpperCAmelCase_:		str							      =     bert.half()

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		Optional[int]							      =     AltDiffusionImgaImgPipeline(
						    unet=SCREAMING_SNAKE_CASE_,   scheduler=SCREAMING_SNAKE_CASE_,   vae=SCREAMING_SNAKE_CASE_,   text_encoder=SCREAMING_SNAKE_CASE_,   tokenizer=SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   feature_extractor=self.dummy_extractor,   )
						UpperCAmelCase_:		Optional[Any]							      =     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,   do_normalize=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     alt_pipe.to(SCREAMING_SNAKE_CASE_					)
						alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Optional[Any]							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     alt_pipe(
						    [prompt],   generator=SCREAMING_SNAKE_CASE_,   num_inference_steps=2,   output_type="""np""",   image=SCREAMING_SNAKE_CASE_,   ).images

						assert image.shape == (1, 32, 32, 3)

			@unittest.skipIf(torch_device != """cuda""",   """This test requires a GPU"""					)
			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/img2img/sketch-mountains-input.jpg"""					)
						# resize to resolution that is divisible by 8 but not 16 or 32
						UpperCAmelCase_:		List[Any]							      =     init_image.resize((760, 504)					)

						UpperCAmelCase_:		Tuple							      =     """BAAI/AltDiffusion"""
						UpperCAmelCase_:		Tuple							      =     AltDiffusionImgaImgPipeline.from_pretrained(
						    SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   )
						pipe.to(SCREAMING_SNAKE_CASE_					)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)
						pipe.enable_attention_slicing()

						UpperCAmelCase_:		Tuple							      =     """A fantasy landscape, trending on artstation"""

						UpperCAmelCase_:		Optional[int]							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		int							      =     pipe(
						    prompt=SCREAMING_SNAKE_CASE_,   image=SCREAMING_SNAKE_CASE_,   strength=0.7_5,   guidance_scale=7.5,   generator=SCREAMING_SNAKE_CASE_,   output_type="""np""",   )
						UpperCAmelCase_:		Dict							      =     output.images[0]

						UpperCAmelCase_:		Tuple							      =     image[255:258, 383:386, -1]

						assert image.shape == (504, 760, 3)
						UpperCAmelCase_:		str							      =     np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







@slow
@require_torch_gpu
class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   int:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Optional[Any]							      =     load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/img2img/sketch-mountains-input.jpg"""					)
						UpperCAmelCase_:		Optional[Any]							      =     init_image.resize((768, 512)					)
						UpperCAmelCase_:		Union[str, Any]							      =     load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""					)

						UpperCAmelCase_:		List[str]							      =     """BAAI/AltDiffusion"""
						UpperCAmelCase_:		int							      =     AltDiffusionImgaImgPipeline.from_pretrained(
						    SCREAMING_SNAKE_CASE_,   safety_checker=SCREAMING_SNAKE_CASE_,   )
						pipe.to(SCREAMING_SNAKE_CASE_					)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)
						pipe.enable_attention_slicing()

						UpperCAmelCase_:		Dict							      =     """A fantasy landscape, trending on artstation"""

						UpperCAmelCase_:		int							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		int							      =     pipe(
						    prompt=SCREAMING_SNAKE_CASE_,   image=SCREAMING_SNAKE_CASE_,   strength=0.7_5,   guidance_scale=7.5,   generator=SCREAMING_SNAKE_CASE_,   output_type="""np""",   )
						UpperCAmelCase_:		Dict							      =     output.images[0]

						assert image.shape == (512, 768, 3)
						# img2img is flaky across GPUs even in fp32, so using MAE here
						assert np.abs(expected_image - image					).max() < 1E-2






